Markerless motion capture (MMC) has received increasing attention in the last years due to its potential in several fields such as biomechanics, clinical and sport applications, entertainment (animation, movies and gaming) and surveillance. For biomechanical, clinical and sport applications, accuracy is of paramount importance. Accurate measurement of human body kinematics is most likely achieved using for the MMC tracking a subject specific model. The subject specific model includes information on both body shape and joint centers locations.
Models for MMC have been generated in the past using passive methods since they can be implemented with the same video system used for motion capture, making the clinical use simpler and not requiring further dedicated hardware. For example, passive methods for 3D human body model acquisition have used three cameras in mutually orthogonal views that required the subject to perform a precise set of movements and constructed a model though the parametric composition of primitives. Another approach used four views where extremes were used to find feature points. This method was simple and efficient but provided only limited information on joint centers since it did not provide a solution to find elbow or wrist joints and there was no assessment of the accuracy of the method. Functional methods represent an alternative to the identification of some of the joints of the human body. They consist on estimating the center of rotation (or pivot point) between two adjacent anatomical segments by tracking the relevant motion during a specific movement. A functional approach was for example used in the identification of the joint centers of several main joints of the human body. However the model was lacking ankle and wrist joints and was lacking biomechanical rigor in aligning the joint centers to a reference pose. Moreover, the method did not provide seamless mesh of the model surface with equally spaced points, which is in general necessary for MMC tracking algorithms based on mesh registration.
Several approaches used a general geometric representation of the body segments where ellipsoidal meta-balls were used. However, to reconstruct the subject body shape with the best fidelity possible requires a free form surface. Another report discusses a seamless, robust and efficient method to generate a human model although that study did not provide automated identification of the joint centers location. Still in another report, one used a free form surface to model the upper body that required multiple subject poses. A seminal work for very accurate model generation used a space of human shapes database. However the algorithm needed manual initialization of data and model mesh correspondences and has been demonstrated to work only on laser scan quality data.
Therefore there remains an important need for automatic, accurate and efficient model generation method that can be applied to meshes with different quality ranging highly accurate laser scan meshes to coarse video based 3D reconstructions. Accordingly, the purpose of this invention was to develop and test a method capable to provide seamless free form surface model of the human body shape of the subject together with joint center locations, using just one-static capture of the pose of the subject or as few as one-static capture of the pose of the subject.